Advanced Lane Finding Project¶
The goals / steps of this project are the following:
- Compute the camera calibration matrix and distortion coefficients given a set of chessboard images.
- Apply a distortion correction to raw images.
- Use color transforms, gradients, etc., to create a thresholded binary image.
- Apply a perspective transform to rectify binary image ("birds-eye view").
- Detect lane pixels and fit to find the lane boundary.
- Determine the curvature of the lane and vehicle position with respect to center.
- Warp the detected lane boundaries back onto the original image.
- Output visual display of the lane boundaries and numerical estimation of lane curvature and vehicle position.
First, I'll compute the camera calibration using chessboard images¶
In [1]:
import numpy as np
import cv2
import glob
import os
from moviepy.editor import VideoFileClip
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
from IPython.display import HTML
# qt didn't work, took inline from project 1
%matplotlib inline
import math
In [2]:
def color_gray(img):
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
return gray
In [3]:
nx = 9
ny = 6
objp = np.zeros((6*9,3), np.float32)
objp[:,:2] = np.mgrid[0:9,0:6].T.reshape(-1,2)
# Arrays to store object points and image points from all
# the images.
objpoints = [] # 3d points in real world space
imgpoints = [] # 2d points in image plane.
# Make a list of calibration images
images = glob.glob('camera_cal/calibration*.jpg')
# Step through the list and search for chessboard corners
for fname in images:
img = cv2.imread(fname)
gray = color_gray(img)
# Find the chessboard corners
ret, corners = cv2.findChessboardCorners(gray, (nx, ny),None)
# If found, add object points, image points
if ret == True:
objpoints.append(objp)
imgpoints.append(corners)
Undistort the images¶
In [4]:
def cal_undistort(img, objpoints, imgpoints):
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None)
dist = cv2.undistort(img, mtx, dist, None, mtx)
return dist
img1 = cv2.imread('camera_cal/calibration1.jpg')
img2 = cv2.imread('camera_cal/calibration1.jpg')
#Checkboard
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(img1)
ax1.set_title('Original Image', fontsize=50)
ax2.imshow(cal_undistort(img2, objpoints, imgpoints))
ax2.set_title('Undistorted Image', fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
1. Briefly state how you computed the camera matrix and distortion coefficients. Provide an example of a distortion corrected calibration image.¶
The code that I wrote to get the undistorted images I used a combination of code. Taking out the color_gray as it's own function, using cv2 functions like calibrateCamera shapes the image into the example shown above.
In [5]:
img3 = mpimg.imread('test_images/8.jpg')
#Roads
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(img3)
ax1.set_title('Original Image', fontsize=50)
ax2.imshow(cal_undistort(img3, objpoints, imgpoints))
ax2.set_title('Undistorted Image', fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
2. Provide an example of a distortion-corrected image.¶
An example of the undistorted image is shown above.
Sobel Operator¶
In [6]:
def abs_sobel_thresh(img, orient='x', thresh_min=0, thresh_max=255):
gray = color_gray(img)
if orient == 'x':
abs_sobel = np.absolute(cv2.Sobel(gray, cv2.CV_64F, 1, 0))
elif orient == 'y':
abs_sobel = np.absolute(cv2.Sobel(gray, cv2.CV_64F, 0, 1))
scaled_sobel = np.uint8(255*abs_sobel/np.max(abs_sobel))
binary_output = np.zeros_like(scaled_sobel)
binary_output[(scaled_sobel >= thresh_min) & (scaled_sobel <= thresh_max)] = 1
return binary_output
grad_binary = abs_sobel_thresh(img3, orient='x', thresh_min=20, thresh_max=100)
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(img3)
ax1.set_title('Original Image', fontsize=50)
ax2.imshow(grad_binary, cmap='gray')
ax2.set_title('Sobel Operator Image', fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
Magnitude of the Gradient¶
In [7]:
def mag_thresh(img, sobel_kernel=3, mag_thresh=(0, 255)):
gray = color_gray(img)
sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel)
sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel)
gradmag = np.sqrt(sobelx**2 + sobely**2)
scale_factor = np.max(gradmag)/255
gradmag = (gradmag/scale_factor).astype(np.uint8)
binary_output = np.zeros_like(gradmag)
binary_output[(gradmag >= mag_thresh[0]) & (gradmag <= mag_thresh[1])] = 1
return binary_output
mag_binary = mag_thresh(img3, sobel_kernel=3, mag_thresh=(30, 100))
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(img3)
ax1.set_title('Original Image', fontsize=50)
ax2.imshow(mag_binary, cmap='gray')
ax2.set_title('Magnitude of the Gradient Image', fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
Direction of the Gradient¶
In [8]:
def dir_threshold(img, sobel_kernel=3, thresh=(0, np.pi/2)):
gray = color_gray(img)
sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel)
sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel)
absgraddir = np.arctan2(np.absolute(sobely), np.absolute(sobelx))
binary_output = np.zeros_like(absgraddir)
binary_output[(absgraddir >= thresh[0]) & (absgraddir <= thresh[1])] = 1
return binary_output
dir_binary = dir_threshold(img3, sobel_kernel=15, thresh=(0.7, 1.3))
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(img3)
ax1.set_title('Original Image', fontsize=50)
ax2.imshow(dir_binary, cmap='gray')
ax2.set_title('Direction of the Gradient Image', fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
HLS Thresholds¶
In [9]:
def hls_select(img, thresh=(0, 255)):
hls = cv2.cvtColor(img, cv2.COLOR_RGB2HLS)
s_channel = hls[:,:,2]
binary_output = np.zeros_like(s_channel)
binary_output[(s_channel > thresh[0]) & (s_channel <= thresh[1])] = 1
return binary_output
hls_binary = hls_select(img3, thresh=(90, 255))
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(img3)
ax1.set_title('Original Image', fontsize=50)
ax2.imshow(hls_binary, cmap='gray')
ax2.set_title('HLS Thresholds Image', fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
Combined Color and Gradient¶
In [10]:
def pipeline(img, s_thresh=(170, 255), sx_thresh=(20, 100)):
gradx = abs_sobel_thresh(img, orient='x', thresh_min=20, thresh_max=100)
grady = abs_sobel_thresh(img, orient='y', thresh_min=20, thresh_max=100)
mag_binary = mag_thresh(img, sobel_kernel=3, mag_thresh=(30, 100))
dir_binary = dir_threshold(img, sobel_kernel=15, thresh=(0.7, 1.3))
hls_binary = hls_select(img, thresh=(90, 255))
combined = np.zeros_like(dir_binary)
combined[((gradx == 1) & (grady == 1)) | ((hls_binary == 1) & (dir_binary == 1))] = 1
return combined
result = pipeline(img3)
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(img3)
ax1.set_title('Original Image', fontsize=50)
ax2.imshow(result, cmap='gray')
ax2.set_title('Combined Color and Gradient', fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
3. Describe how (and identify where in your code) you used color transforms, gradients or other methods to create a thresholded binary image. Provide an example of a binary image result.¶
Examples of each threshold and gradients are shown above. Examples are the following: Sobel operator, Magnitude of the Gradient, Direction of the Gradient, Combining Thresholds, and HLS Thresholds. The combined images are shown with the above photo.
Perspective Images¶
In [11]:
def perspective_image(img):
h = img.shape[0] #720
w = img.shape[1] #1280
src = np.float32([[570, 470], [750, 470], [1130, 690], [270, 690]])
dst = np.float32([[200, 0], [1080, 0], [1080, 720], [200, 720]])
M = cv2.getPerspectiveTransform(src, dst)
warped = cv2.warpPerspective(img, M, (w, h), flags=cv2.INTER_LINEAR)
return warped
combination = perspective_image(img3)
binary_warped = perspective_image(pipeline(img3))
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(20, 10))
ax1.set_title('Source Image', fontsize=30)
ax1.imshow(img3)
ax2.set_title('Bird\'s View Original Image', fontsize=30)
ax2.imshow(combination, cmap='gray')
Out[11]:
In [12]:
images = glob.glob('test_images/*.jpg')
for image in images:
img = mpimg.imread(image)
imgbv = perspective_image(pipeline(img))
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(20, 10))
ax1.set_title('Bird\'s View Original Image', fontsize=30)
ax1.imshow(img)
ax2.set_title('Bird\'s View Perspective Image', fontsize=30)
ax2.imshow(imgbv, cmap='gray')
4. Describe how (and identify where in your code) you performed a perspective transform and provide an example of a transformed image.¶
The above code shown performs the perspective image to show the birds eye view. Using the src and dst points helps to pinpoint exactly where we need the car to drive.
In [13]:
original_image_histogram = np.sum(combination[combination.shape[0]//2:,:], axis=0)
warped_image_histogram = np.sum(binary_warped[binary_warped.shape[0]//2:,:], axis=0)
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(20, 10))
ax1.set_title('Bird\'s View Original Histogram', fontsize=30)
ax1.plot(original_image_histogram)
ax2.set_title('Bird\'s View Perspective Histogram', fontsize=30)
ax2.plot(warped_image_histogram)
Out[13]:
In [14]:
class Line():
def __init__(self):
# was the line detected in the last iteration?
self.detected = False
# x values of the last n fits of the line
self.recent_xfitted = []
#average x values of the fitted line over the last n iterations
self.bestx = None
#polynomial coefficients averaged over the last n iterations
self.best_fit = None
#polynomial coefficients for the most recent fit
self.current_fit = [np.array([False])]
#radius of curvature of the line in some units
self.radius_of_curvature = None
#distance in meters of vehicle center from the line
self.line_base_pos = None
#difference in fit coefficients between last and new fits
self.diffs = np.array([0,0,0], dtype='float')
#x values for detected line pixels
self.allx = None
#y values for detected line pixels
self.ally = None
# Checking the mean of all lines
def sanity_check(self, recent):
if len(self.recent_xfitted) > 1:
mean = np.mean(np.abs(self.bestx - recent))
return mean
# update the lines if they don't follow
def update(self, current, recent):
if (len(self.current_fit) <= 1):
self.current_fit.append(current)
self.recent_xfitted.append(recent)
else:
self.current_fit.append(current)
self.recent_xfitted.append(recent)
del self.current_fit[0]
del self.recent_xfitted[0]
self.bestx = np.mean(self.recent_xfitted, axis=0)
self.best_fit = np.mean(self.current_fit, axis=0)
Finding Lanes¶
In [15]:
#Calling Line class
left_line = Line()
right_line = Line()
#Create a def for easier use
def finding_lanes(binary_warped, newimg):
histogram = np.sum(binary_warped[binary_warped.shape[0]//2:,:], axis=0)
out_img = np.dstack((binary_warped, binary_warped, binary_warped))*255
midpoint = np.int(histogram.shape[0]/2)
leftx_base = np.argmax(histogram[:midpoint])
rightx_base = np.argmax(histogram[midpoint:]) + midpoint
nwindows = 9
window_height = np.int(binary_warped.shape[0]/nwindows)
nonzero = binary_warped.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
leftx_current = leftx_base
rightx_current = rightx_base
margin = 100
minpix = 50
left_lane_inds = []
right_lane_inds = []
for window in range(nwindows):
win_y_low = binary_warped.shape[0] - (window+1)*window_height
win_y_high = binary_warped.shape[0] - window*window_height
win_xleft_low = leftx_current - margin
win_xleft_high = leftx_current + margin
win_xright_low = rightx_current - margin
win_xright_high = rightx_current + margin
cv2.rectangle(out_img,(win_xleft_low,win_y_low),(win_xleft_high,win_y_high),(0,255,0), 2)
cv2.rectangle(out_img,(win_xright_low,win_y_low),(win_xright_high,win_y_high),(0,255,0), 2)
good_left_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xleft_low) & (nonzerox < win_xleft_high)).nonzero()[0]
good_right_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xright_low) & (nonzerox < win_xright_high)).nonzero()[0]
left_lane_inds.append(good_left_inds)
right_lane_inds.append(good_right_inds)
if len(good_left_inds) > minpix:
leftx_current = np.int(np.mean(nonzerox[good_left_inds]))
if len(good_right_inds) > minpix:
rightx_current = np.int(np.mean(nonzerox[good_right_inds]))
left_lane_inds = np.concatenate(left_lane_inds)
right_lane_inds = np.concatenate(right_lane_inds)
leftx = nonzerox[left_lane_inds]
lefty = nonzeroy[left_lane_inds]
rightx = nonzerox[right_lane_inds]
righty = nonzeroy[right_lane_inds]
left_fit = np.polyfit(lefty, leftx, 2)
right_fit = np.polyfit(righty, rightx, 2)
ym_per_pix = 30/720
xm_per_pix = 3.7/700
left_fit_cr = np.polyfit(lefty*ym_per_pix, leftx*xm_per_pix, 2)
right_fit_cr = np.polyfit(righty*ym_per_pix, rightx*xm_per_pix, 2)
yvals = range(0, img.shape[0])
leftCurverad = ((1 + (2*left_fit_cr[0]*yvals*ym_per_pix + left_fit_cr[1])**2)**1.5) / np.absolute(2*left_fit_cr[0])
rightCurverad = ((1 + (2*right_fit_cr[0]*yvals*ym_per_pix + right_fit_cr[1])**2)**1.5) / np.absolute(2*right_fit_cr[0])
curverad = (leftCurverad + rightCurverad)/2
ploty = np.linspace(0, binary_warped.shape[0]-1, binary_warped.shape[0])
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
righty = right_line.sanity_check(right_fitx)
lefty = left_line.sanity_check(left_fitx)
if len(right_line.recent_xfitted) > 1:
if righty < 100:
right_line.update(right_fit, right_fitx)
if lefty < 100:
left_line.update(left_fit, left_fitx)
else:
right_line.update(right_fit, right_fitx)
left_line.update(left_fit, left_fitx)
left_fitx = left_line.best_fit[0]*ploty**2 + left_line.best_fit[1]*ploty + left_line.best_fit[2]
right_fitx = right_line.best_fit[0]*ploty**2 + right_line.best_fit[1]*ploty + right_line.best_fit[2]
warp_zero = np.zeros_like(binary_warped).astype(np.uint8)
color_warp = np.dstack((warp_zero, warp_zero, warp_zero))
pts_left = np.array([np.transpose(np.vstack([left_fitx, ploty]))])
pts_right = np.array([np.flipud(np.transpose(np.vstack([right_fitx, ploty])))])
pts = np.hstack((pts_left, pts_right))
cv2.fillPoly(color_warp, np.int_([pts]), (0,255, 0))
newwarp = cv2.warpPerspective(color_warp, Minv, (img.shape[1], img.shape[0]))
result = cv2.addWeighted(newimg, 1, newwarp, 0.3, 1)
camera_center = (left_fitx[-1] + right_fitx[-1])/2
rightcurv = "Right Radius of Curvature = %.2f m" % np.average([rightCurverad])
leftcurv = "Left Radius of Curvature = %.2f m" % np.average([leftCurverad])
center = "Vehicle is %.2f m of center" % ((camera_center - 1280/2) * xm_per_pix)
cv2.putText(result, leftcurv, (50, 50), 1, 3, (255,255,255), thickness=4)
cv2.putText(result, rightcurv, (50, 100), 1, 3, (255,255,255), thickness=4)
cv2.putText(result, center, (50, 150), 1, 3, (255,255,255), thickness=4)
return result
In [16]:
#Here is what the code does.
#def finding_lanes(binary_warped):
# Assuming you have created a warped binary image called "binary_warped"
# Take a histogram of the bottom half of the image
histogram = np.sum(binary_warped[binary_warped.shape[0]//2:,:], axis=0)
# Create an output image to draw on and visualize the result
out_img = np.dstack((binary_warped, binary_warped, binary_warped))*255
# Find the peak of the left and right halves of the histogram
# These will be the starting point for the left and right lines
midpoint = np.int(histogram.shape[0]/2)
leftx_base = np.argmax(histogram[:midpoint])
rightx_base = np.argmax(histogram[midpoint:]) + midpoint
# Choose the number of sliding windows
nwindows = 9
# Set height of windows
window_height = np.int(binary_warped.shape[0]/nwindows)
# Identify the x and y positions of all nonzero pixels in the image
nonzero = binary_warped.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
# Current positions to be updated for each window
leftx_current = leftx_base
rightx_current = rightx_base
# Set the width of the windows +/- margin
margin = 100
# Set minimum number of pixels found to recenter window
minpix = 50
# Create empty lists to receive left and right lane pixel indices
left_lane_inds = []
right_lane_inds = []
# Step through the windows one by one
for window in range(nwindows):
# Identify window boundaries in x and y (and right and left)
win_y_low = binary_warped.shape[0] - (window+1)*window_height
win_y_high = binary_warped.shape[0] - window*window_height
win_xleft_low = leftx_current - margin
win_xleft_high = leftx_current + margin
win_xright_low = rightx_current - margin
win_xright_high = rightx_current + margin
# Draw the windows on the visualization image
cv2.rectangle(out_img,(win_xleft_low,win_y_low),(win_xleft_high,win_y_high),(0,255,0), 2)
cv2.rectangle(out_img,(win_xright_low,win_y_low),(win_xright_high,win_y_high),(0,255,0), 2)
# Identify the nonzero pixels in x and y within the window
good_left_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xleft_low) & (nonzerox < win_xleft_high)).nonzero()[0]
good_right_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xright_low) & (nonzerox < win_xright_high)).nonzero()[0]
# Append these indices to the lists
left_lane_inds.append(good_left_inds)
right_lane_inds.append(good_right_inds)
# If you found > minpix pixels, recenter next window on their mean position
if len(good_left_inds) > minpix:
leftx_current = np.int(np.mean(nonzerox[good_left_inds]))
if len(good_right_inds) > minpix:
rightx_current = np.int(np.mean(nonzerox[good_right_inds]))
# Concatenate the arrays of indices
left_lane_inds = np.concatenate(left_lane_inds)
right_lane_inds = np.concatenate(right_lane_inds)
# Extract left and right line pixel positions
leftx = nonzerox[left_lane_inds]
lefty = nonzeroy[left_lane_inds]
rightx = nonzerox[right_lane_inds]
righty = nonzeroy[right_lane_inds]
# Fit a second order polynomial to each
left_fit = np.polyfit(lefty, leftx, 2)
right_fit = np.polyfit(righty, rightx, 2)
In [17]:
# Generate x and y values for plotting
ploty = np.linspace(0, binary_warped.shape[0]-1, binary_warped.shape[0] )
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
out_img[nonzeroy[left_lane_inds], nonzerox[left_lane_inds]] = [255, 0, 0]
out_img[nonzeroy[right_lane_inds], nonzerox[right_lane_inds]] = [0, 0, 255]
plt.imshow(out_img)
plt.plot(left_fitx, ploty, color='yellow')
plt.plot(right_fitx, ploty, color='yellow')
plt.xlim(0, 1280)
plt.ylim(720, 0)
Out[17]:
5. Describe how (and identify where in your code) you identified lane-line pixels and fit their positions with a polynomial?¶
The picture above shows the curves in the line, the method used was
left_fitx = left_fit[0]ploty**2 + left_fit[1]ploty + left_fit[2]
right_fitx = right_fit[0]ploty**2 + right_fit[1]ploty + right_fit[2]
This helped in solving the curves in the line
Getting the Margin of Previous line Position¶
In [18]:
# Assume you now have a new warped binary image
# from the next frame of video (also called "binary_warped")
# It's now much easier to find line pixels!
nonzero = binary_warped.nonzero()
nonzeroy = np.array(nonzero[0])
nonzerox = np.array(nonzero[1])
margin = 100
left_lane_inds = ((nonzerox > (left_fit[0]*(nonzeroy**2) + left_fit[1]*nonzeroy + left_fit[2] - margin)) & (nonzerox < (left_fit[0]*(nonzeroy**2) + left_fit[1]*nonzeroy + left_fit[2] + margin)))
right_lane_inds = ((nonzerox > (right_fit[0]*(nonzeroy**2) + right_fit[1]*nonzeroy + right_fit[2] - margin)) & (nonzerox < (right_fit[0]*(nonzeroy**2) + right_fit[1]*nonzeroy + right_fit[2] + margin)))
# Again, extract left and right line pixel positions
leftx = nonzerox[left_lane_inds]
lefty = nonzeroy[left_lane_inds]
rightx = nonzerox[right_lane_inds]
righty = nonzeroy[right_lane_inds]
# Fit a second order polynomial to each
left_fit = np.polyfit(lefty, leftx, 2)
right_fit = np.polyfit(righty, rightx, 2)
# Generate x and y values for plotting
ploty = np.linspace(0, binary_warped.shape[0]-1, binary_warped.shape[0] )
left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
In [19]:
# Create an image to draw on and an image to show the selection window
out_img = np.dstack((binary_warped, binary_warped, binary_warped))*255
window_img = np.zeros_like(out_img)
# Color in left and right line pixels
out_img[nonzeroy[left_lane_inds], nonzerox[left_lane_inds]] = [255, 0, 0]
out_img[nonzeroy[right_lane_inds], nonzerox[right_lane_inds]] = [0, 0, 255]
# Generate a polygon to illustrate the search window area
# And recast the x and y points into usable format for cv2.fillPoly()
left_line_window1 = np.array([np.transpose(np.vstack([left_fitx-margin, ploty]))])
left_line_window2 = np.array([np.flipud(np.transpose(np.vstack([left_fitx+margin, ploty])))])
left_line_pts = np.hstack((left_line_window1, left_line_window2))
right_line_window1 = np.array([np.transpose(np.vstack([right_fitx-margin, ploty]))])
right_line_window2 = np.array([np.flipud(np.transpose(np.vstack([right_fitx+margin, ploty])))])
right_line_pts = np.hstack((right_line_window1, right_line_window2))
# Draw the lane onto the warped blank image
cv2.fillPoly(window_img, np.int_([left_line_pts]), (0,255, 0))
cv2.fillPoly(window_img, np.int_([right_line_pts]), (0,255, 0))
result = cv2.addWeighted(out_img, 1, window_img, 0.3, 0)
plt.imshow(result)
plt.plot(left_fitx, ploty, color='yellow')
plt.plot(right_fitx, ploty, color='yellow')
plt.xlim(0, 1280)
plt.ylim(720, 0)
Out[19]:
In [20]:
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None)
newimg = cv2.undistort(img, mtx, dist, None, mtx)
ym_per_pix = 10/720
xm_per_pix = 4/384
yvals = range(0, img.shape[0])
res_yvals = np.arange(img.shape[0]-(80/2),0,-80)
left_fit_cr = np.polyfit(lefty*ym_per_pix, leftx*xm_per_pix, 2)
right_fit_cr = np.polyfit(righty*ym_per_pix, rightx*xm_per_pix, 2)
src = np.float32([[570, 470], [750, 470], [1130, 690], [270, 690]])
dst = np.float32([[200, 0], [1080, 0], [1080, 720], [200, 720]])
#M = cv2.getPerspectiveTransform(src, dst)
Minv = cv2.getPerspectiveTransform(dst, src)
warp_zero = np.zeros_like(binary_warped).astype(np.uint8)
color_warp = np.dstack((warp_zero, warp_zero, warp_zero))
pts_left = np.array([np.transpose(np.vstack([left_fitx, ploty]))])
pts_right = np.array([np.flipud(np.transpose(np.vstack([right_fitx, ploty])))])
pts = np.hstack((pts_left, pts_right))
cv2.fillPoly(color_warp, np.int_([pts]), (0,255, 0))
newwarp = cv2.warpPerspective(color_warp, Minv, (img.shape[1], img.shape[0]))
result = cv2.addWeighted(newimg, 1, newwarp, 0.3, 0)
6. Describe how (and identify where in your code) you calculated the radius of curvature of the lane and the position of the vehicle with respect to center.¶
The radius of the curvature of the lanes were calculated as so.
leftCurverad = ((1 + (2left_fit_cr[0]yvalsym_per_pix + left_fit_cr[1])2)1.5) / np.absolute(2left_fit_cr[0])
rightCurverad = ((1 + (2right_fit_cr[0]yvalsym_per_pix + right_fit_cr[1])2)1.5) / np.absolute(2right_fit_cr[0])
curverad = (leftCurverad + rightCurverad)/2
These lines of code helped in identifying where the curvature of lane and position of the vechile is.
In [21]:
def newPipeline(img):
newimg = cal_undistort(img, objpoints, imgpoints)
newBinary = pipeline(newimg)
binary_warped = perspective_image(newBinary)
results = finding_lanes(binary_warped, newimg)
return results
7. Provide an example image of your result plotted back down onto the road such that the lane area is identified clearly.¶
The result of plotting down the lanes, where the road is clearly identified is shown below.
In [22]:
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(20, 10))
f.tight_layout()
ax1.imshow(img3)
ax1.set_title('Original Image', fontsize=30)
ax2.imshow(result, cmap='gray')
ax2.set_title('Find the Lanes Image', fontsize=30)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
8. Provide a link to your final video output. Your pipeline should perform reasonably well on the entire project video (wobbly lines are ok but no catastrophic failures that would cause the car to drive off the road!)¶
Example of the video is shown below.
In [23]:
output_name = "output_images/project_video.mp4"
clip1 = VideoFileClip("project_video.mp4")
white_clip = clip1.fl_image(newPipeline)
%time white_clip.write_videofile(output_name, audio=False)
In [24]:
HTML("""
<video width="960" height="540" controls>
<source src="{0}">
</video>
""".format(output_name))
Out[24]:
9. Briefly discuss any problems / issues you faced in your implementation of this project. Where will your pipeline likely fail? What could you do to make it more robust?¶
Had issues with slice indices must be integers or None or have an index method. figured out that it was cv2 issue with mpimg. Converted the image to solve it. To make it more roboust is to classify the code in a seperate python files. This will help in tweeking the code to certain parameters instead of fixing every single piece of the code. This would make it cleaner and more roboust.